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Wang et al. Microstructures 2023;3:2023036 https://dx.doi.org/10.20517/microstructures.2023.27 Page 3 of 12

pattern is automatically assigned based on its average intensity. Our results show that Auto-CLAHE
significantly improves the signal-to-noise ratio of low-intensity diffraction spots, particularly those from
patterns acquired away from zone axes. This improvement in diffraction pattern quality enables better
template matching, resulting in less noise and more accurate orientation mapping.

MATERIALS AND METHODS
An indented pure magnesium (Mg) TEM specimen was used as the model system to test the Auto-CLAHE
algorithm. The bulk sample was a hot-rolled commercially pure Mg purchased from MetalMart
International Inc. The sample was annealed at 500 °C for two hours under an argon flow atmosphere to
homogenize the microstructure and remove most of the dislocations, then mechanically polished to 1,200
grit SiC abrasive sandpaper and chemically polished in a 10% nitric acid in methanol before
nanoindentation. Nanoindentation was carried out using a KLA iMicro nanoindentation testing system
(KLA Instruments) equipped with a Berkovich tip. Nanoindentation was performed on a grain close to the
[0001] direction so the indentation is approximately parallel to the c-axis of the crystal. The indentation
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depth was 600 nm. The focused ion beam (FIB, Helios G4, ThermoFisher, 30 kV Ga beam) lift-out method
was used to prepare the cross-sectional TEM specimen to investigate the microstructure under the indent
impression. The TEM characterizations were performed in an FEI Tecnai TEM operated at 200 keV and
equipped with a NanoMEGAS ASTAR system with a Stingray CCD camera for PED experiments. The
precession angle was 0.3° and the step size was 20 nm when acquiring the map. The 0.3° precession angle
was selected because it offers a good combination of reduction in the dynamical effect and retaining a
decent beam spot size (~3 nm). One precession per pixel was used, and the diffraction pattern rate was
approximately 0.06 s per pixel.

The Auto-CLAHE method was developed as a Python program, utilizing several scientific libraries to
handle data. The source code can be found in our GitHub repository linked under the “Availability of data
and materials” section. In the code, the HyperSpy and PixSTEM libraries are used to open and read the PED
raw data .blo files. The average intensity of each diffraction pattern is calculated. The diffraction patterns are
recorded as 8-bit images. The pixel intensity ranges from 0 (dark) to 255 (bright). Next, each diffraction
pattern is processed with CLAHE (from the OpenCV library). The clip limit value is calculated using the
equation:

There is an inverse relationship between the clip limit value and the average intensity of the diffraction
pattern. If the diffraction pattern is at or close to a zone axis, many diffraction spots are excited. The average
intensity of the pattern is high, the clip limit is small, and little enhancement is required. In contrast, if the
diffraction pattern is far away from zone axes, a few diffraction spots are excited. The average intensity of
the pattern is low, the clip limit is large, and signal enhancement is applied. In the equation, the numerator
coefficient of 10 in the Auto-CLAHE algorithm was determined by a combination of trial and error and
visual inspection to ensure optimal quality of the enhanced diffraction patterns. It is worth noting that if the
average intensity of a diffraction pattern is too high, the “10/Average_intensity” term may round down to 0.
Using it alone as the clip limit can cause the CLAHE algorithm to default to non-CLAHE, which will
produce erroneous results. To address this issue, another coefficient of 1 was introduced to help preserve
image information when processing bright diffraction patterns. With this setup, we have successfully
applied this approach to generate enhanced diffraction patterns in various datasets, but users can adjust the
coefficients based on their own PED datasets.